Evaluating methods for grouping and comparing crash dumps
Abstract: Observations suggest that a high percentage of all reported software errors are reoccurrences. In certain cases even as high as 75%. This high percentage of reoccurrences means that companies are wasting hours manually rediagnosing errors that have already been diagnosed. The goal of this thesis was to eliminate or limit cases where errors have to be re-diagnosed through the use of automated grouping of crash dumps. In this study we constructed a series of tests. We evaluate both pre-existing methods as well as our new proposed methods for comparing and matching crash dumps. A set of known errors were used as basis for measuring the matching precision and grouping ability of each method. Our results show a large variation in accuracy between methods and that generally, the more accurate a method is, the less it offers in terms of grouping ability. With an accuracy ranging from 50% to 90% and a reduction in manual diagnosis by up to 90%, we have shown that through automatic grouping of crash dumps we can accurately identify reoccurrences and reduce manual diagnosis.
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